华东师范大学学报（自然科学版） >

2025 , Vol. 2025 >Issue 1: 151 - 164

DOI: https://doi.org/10.3969/j.issn.1000-5641.2025.01.012

计算机科学

基于海思Hi3531部署的红外小目标检测算法研究

  • 傅晓雪 ,
  • 黄昶
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  • 华东师范大学 通信与电子工程学院, 上海　200241
黄　昶, 男, 副教授, 硕士生导师, 研究方向为图像处理. E-mail: chuang@ee.ecnu.edu.cn

收稿日期: 2024-01-15

  网络出版日期: 2025-01-20

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华东师范大学学报期刊社, 2025, 版权所有，未经授权，不得转载、摘编本刊文章，不得使用本刊的版式设计。
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Infrared small target detection algorithm deployed on HiSilicon Hi3531

  • Xiaoxue FU ,
  • Chang HUANG
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  • School of Communication and Electronic Engineering, East China Normal University, Shanghai　200241, China

Received date: 2024-01-15

  Online published: 2025-01-20

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, 2025, Copyright reserved © 2025.
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摘要

针对现有算法计算量大、实时性差、部署困难等问题, 同时为满足红外探测系统对实时性及准确率的高要求, 提出了一种部署于国产嵌入式芯片的轻量化算法, 即YOLOv5-TinyHisi. YOLOv5-TinyHisi算法根据红外小目标特点对主干网络结构进行轻量化改造, 并使用SIoU优化损失函数中的边界误差, 提高了红外小目标定位的准确性. 将YOLOv5-TinyHisi算法模型部署到海思Hi3531DV200嵌入式开发板上, 利用芯片集成的神经网络加速引擎 (neural network inference engine, NNIE) 对网络推理进行加速. 在公开数据集上的实验结果表明, 该算法能够大幅度降低参数量和模型大小, 与YOLOv5相比, 在平均精度上的提升了1.52%. 在海思Hi3531DV200嵌入式开发板上对分辨率为 (1280×512)像素的单张图像推理速度可达到35帧/s, 召回率可达到95%, 满足了红外探测系统对实时性和准确率的要求.

关键词： 红外小目标检测; 嵌入式系统; YOLOv5; 神经网络加速引擎

本文引用格式

傅晓雪 , 黄昶 . 基于海思Hi3531部署的红外小目标检测算法研究[J]. 华东师范大学学报（自然科学版）, 2025 , 2025(1) : 151 -164 . DOI: 10.3969/j.issn.1000-5641.2025.01.012

Abstract

In response to the existing shortcomings of large computational complexity, poor real-time performance, and deployment difficulties in current algorithms, and to meet the high requirements of real-time performance and accuracy for infrared detection systems, proposes a lightweight algorithm deployed on domestically produced embedded chips, termed YOLOv5-TinyHisi. The YOLOv5-TinyHisi algorithm undertakes lightweight modifications to the backbone network structure based on the characteristics of infrared small targets. Additionally, it utilizes SIoU optimized loss function for boundary error, thereby enhancing the accuracy of infrared small target localization. The YOLOv5-TinyHisi algorithm model is deployed on Hi3531DV200, utilizing the chip-integrated neural network inference engine (NNIE) to accelerate network inference. Experimental results on public datasets demonstrate that the algorithm achieves a 1.52% improvement in average precision (mAP) compared to YOLOv5, while significantly reducing parameter count and model size. On the Hi3531DV200, the inference speed for a single image with a resolution of (1280 × 512) pixels reaches 35 frames per second (FPS), with a recall rate of 95%, meeting the real-time and accuracy requirements of the infrared detection system.

Key words： infrared small target detection; embedded system; YOLOv5; neural network inference engine

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